docs(pdm): adds doc about how to install pdm

updates ReadMe with how to install pdm

backend/README.md

@@ -3,7 +3,7 @@
 ## Backend is created with [Django](https://www.djangoproject.com/)
 This project was bootstrapped with  [Geodjango Template](https://github.com/itskshitiz321/geodjangotemplate.git)
 #### For Quickly Getting Started
-**Note:** Depending upon your OS and Env installation will vary, This project tightly depends on [Tensorflow](https://www.tensorflow.org/install/pip) with GPU support so accordingly build your development environment 
+**Note:** Depending upon your OS and Env installation will vary, This project tightly depends on [Tensorflow](https://www.tensorflow.org/install/pip) with GPU support so accordingly build your development environment
 ### Install Python3, pip and virtualenv first
 ##### Skip this, step if you already have one
     sudo apt-get install python3
@@ -19,13 +19,13 @@ This project was bootstrapped with  [Geodjango Template](https://github.com/itsk
 sudo apt-get install git-lfs
 ```
 
-- Clone Ramp Basemodel 
+- Clone Ramp Basemodel
 ```
 git clone https://github.com/radiantearth/model_ramp_baseline.git
 ```
 
-- Clone Ramp - Code 
-Note: This clone location will be your RAMP_HOME 
+- Clone Ramp - Code
+Note: This clone location will be your RAMP_HOME
 ```
 git clone https://github.com/kshitijrajsharma/ramp-code-fAIr.git ramp-code
 ```
@@ -36,19 +36,19 @@ cp -r model_ramp_baseline/data/input/checkpoint.tf ramp-code/ramp/checkpoint.tf
 ```
 
 
-- Remove basemodel repo we don't need it anymore 
+- Remove basemodel repo we don't need it anymore
 ```
 rm -rf model_ramp_baseline
 ```
-- Install numpy 
-Numpy needs to be installed before gdal 
+- Install numpy
+Numpy needs to be installed before gdal
 ```
 pip install numpy==1.23.5
 ```
 
-- Install gdal and rasetrio 
-Based on your env : You can either use conda / setup manually on your os 
-for eg on ubuntu : 
+- Install gdal and rasetrio
+Based on your env : You can either use conda / setup manually on your os
+for eg on ubuntu :
 ```
 sudo add-apt-repository ppa:ubuntugis/ppa && sudo apt-get update
 sudo apt-get install gdal-bin
@@ -58,12 +58,12 @@ export C_INCLUDE_PATH=/usr/include/gdal
 pip install --global-option=build_ext --global-option="-I/usr/include/gdal" GDAL==`gdal-config --version`
 ```
 
-- Install Ramp - Dependecies 
+- Install Ramp - Dependecies
 ```
 cd ramp-code && cd colab && make install
 ```
 
-- For Conda users : You may need to install rtree, gdal , rasterio & imagecodecs separately 
+- For Conda users : You may need to install rtree, gdal , rasterio & imagecodecs separately
 
 ```
 conda install -c conda-forge rtree
@@ -82,14 +82,14 @@ conda install -c conda-forge imagecodecs
 pip install --upgrade setuptools
 ```
 
-- Install fAIr Utilities 
+- Install fAIr Utilities
 ```
 pip install hot-fair-utilities==1.0.41
 ```
 
-**Remember In order to run fAIr , You need to configure your PC with tensorflow - GPU Support** 
+**Remember In order to run fAIr , You need to configure your PC with tensorflow - GPU Support**
 
-You can check your GPU by : 
+You can check your GPU by :
 
 ```
 import tensorflow as tf
@@ -98,82 +98,88 @@ print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('
 
 
 - Install psycopg2
-Again based on your os/env you can do manual installation 
-for eg : on ubuntu : 
+Again based on your os/env you can do manual installation
+for eg : on ubuntu :
 ```
 sudo apt-get install python3-psycopg2
 ```
 
-- Install redis server on your pc 
+- Install redis server on your pc
 
 ```
 sudo apt install redis
 ```
 
-- Finally installl pip dependencies 
+- Install pdm for dependency management
 
 ```
-pip install -r requirements.txt
+pip install pdm
+```
+
+- Finally install project dependencies
+
+```
+pdm install
 ```
 
 ### Make sure you have postgresql installed with postgis extension enabled
 
 
-#### Configure .env: 
+#### Configure .env:
     Create .env in the root backend project , and add the credentials as provided on .env_sample , Export your secret key and database url to your env
 
-    Export your database url 
+    Export your database url
     ```
     export DATABASE_URL=postgis://postgres:postgres@localhost:5432/ai
     ```
-    
-    You will need more env variables (Such as Ramp home, Training Home) that can be found on ```.sample_env```  
+
+    You will need more env variables (Such as Ramp home, Training Home) that can be found on ```.sample_env```
 
 #### Now change your username, password and db name in settings.py accordingly to your database
     python manage.py makemigrations login core
     python manage.py migrate
     python manage.py runserver
-### Now server will be available in your 8000 port on web, you can check out your localhost:8000/admin for admin panel 
+### Now server will be available in your 8000 port on web, you can check out your localhost:8000/admin for admin panel
 To login on admin panel, create your superuser and login with your credentials restarting the server
 
     python manage.py createsuperuser
 
-## Authentication 
+## Authentication
 fAIr uses oauth2.0 Authentication using [osm-login-python](https://github.com/kshitijrajsharma/osm-login-python)
 1. Get your login Url
     Hit ```/api/v1/auth/login/ ```
-    - URL will give you login URL which you can use to provide your osm credentials and authorize fAIr 
+    - URL will give you login URL which you can use to provide your osm credentials and authorize fAIr
     - After successful login  you will get access-token that you can use across all osm login required endpoints in fAIr
-2. Check authentication by getting back your data 
+2. Check authentication by getting back your data
     Hit ```/api/v1/auth/me/```
-    - URL requires access-token as header and in return you will see your osm username, id and image url 
+    - URL requires access-token as header and in return you will see your osm username, id and image url
 
 
-## Start celery workers 
+## Start celery workers
 
--  Start celery workers 
+-  Start celery workers
 
 ```
 celery -A aiproject worker --loglevel=debug -n my_worker
 ```
 
-- Monitor using flower 
-if  you are using redis as result backend, api supports both options django / redis 
+- Monitor using flower
+if  you are using redis as result backend, api supports both options django / redis
 You can start flower to start monitoring your tasks
 ```
-celery -A aiproject  --broker=redis://127.0.0.1:6379/0 flower 
+celery -A aiproject  --broker=redis://127.0.0.1:6379/0 flower
 ```
 
-## Run Tests 
+## Run Tests
 
 ```
 python manage.py test
 ```
 
 
-# Build fAIr with Docker for Development 
-- Install all the required drivers for your graphics to access it from containers, and check your graphics and drivers with ```nvidia-smi``` . Up to now only nvidia is Supported 
-- Follow docker_sample_env to create ```.env``` file in your dir 
+# Build fAIr with Docker for Development
+- Install all the required drivers for your graphics to access it from containers, and check your graphics and drivers with ```nvidia-smi``` . Up to now only nvidia is Supported
+- Follow docker_sample_env to create ```.env``` file in your dir
 - Build the Image
 
 ```